Asiya Khan

QoE Prediction Model and its Application in Video Quality Adaptation Over UMTS Networks

The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

Content Clustering Based Video Quality Prediction Model for MPEG4 Video Streaming over Wireless Networks

The aim of this paper is quality prediction for streaming MPEG4 video sequences over wireless networks for all video content types. Video content has an impact on video quality under same network conditions. This feature has not been widely explored when developing reference-free video quality prediction model for streaming video over wireless or mobile communications. In this paper, we present a two step approach to video quality prediction. First, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, based on the content type, video quality (in terms of Mean Opinion Score) is predicted from network level parameter (packet error rate) and application level (i.e. send bitrate, frame rate) parameters using Principal Component Analysis (PCA). The performance of the developed model is evaluated with unseen datasets and good prediction accuracy is obtained for all content types. The work can help in the development of reference-free video prediction model and priority control for content delivery networks.

Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks

There are many parameters that affect video quality but their combined effect is not well identified and understood when video is transmitted over mobile/ wireless networks. In addition, video content has an impact on video quality under same network conditions. The main aim of this paper is the prediction of video quality combining the application and network level parameters for all content types. Firstly, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, to study and analyze the behaviour of video quality for wide range variations of a set of selected parameters. Finally, to develop two learning models based on – (1) ANFIS to estimate the visual perceptual quality in terms of the Mean Opinion Score (MOS) and decodable frame rate (Q value) and (2) regression modeling to estimate the visual perceptual quality in terms of the MOS. We trained three ANFIS-based ANNs and regression based- models for the three distinct content types using a combination of network and application level parameters and tested the two models using unseen dataset. We confirmed that the video quality is more sensitive to network level compared to application level parameters. Preliminary results show that a good prediction accuracy was obtained from both models. However, the regression based model performed better in terms of the correlation coefficient and the root mean squared error. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over wireless/mobile networks.

Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

Video Quality Prediction Model for H.264 Video over UMTS Networks and Their Application in Mobile Video Streaming

Universal Mobile Telecommunication System (UMTS) is a third generation mobile communication systems that supports wireless wideband multimedia applications. The objective of this paper is to present a new model for non-intrusive prediction of H.264 encoded video quality over UMTS networks and to illustrate their application to video quality monitoring and adaptation in mobile wireless streaming services. First, we present an efficient regression model for predicting video quality non-intrusively for all content types. The model is predicted from a combination of a set of objective parameters in the application and physical layer in terms of the Mean opinion Score (MOS). The application layer parameters considered are the content type, sender bitrate and frame rate and the physical layer parameters are the block error rate modeled with 2-state Markov model for a mean burst length of 1.75. The performance of the proposed metric is evaluated with unseen dataset with good prediction accuracy. Second, we illustrate the application of the model in mobile streaming services by presenting a new Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven. The scheme was tested and evaluated in the NS2 based UMTS simulation network. Extensive simulation results demonstrate the effectiveness of the proposed adaptation scheme in terms of the MOS and especially at the UMTS network bottleneck access where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

Learning models for video quality prediction over wireless local area network and universal mobile telecommunication system networks

Universal mobile telecommunication system (UMTS) is a third-generation mobile communications system that supports wireless wideband multimedia applications. The primary aim of this study is to present learning models based on neural networks for objective, non-intrusive prediction of video quality over wireless local area network (WLAN) and UMTS networks for video applications. The contributions of this study are two-fold: first, an investigation of the impact of parameters both in the application and physical layer on end-to-end video quality is presented. The parameters considered in the application layer are content type (CT), sender bitrate (SBR) and frame rate (FR), whereas in the physical layer block error rate (BLER) and link bandwidth (LBW) are considered. Secondly, learning models based on adaptive neural fuzzy inference system (ANFIS) are developed to predict the visual quality in terms of the mean opinion score for all contents over access networks of UMTS and WLAN. ANFIS is well suited for video quality prediction over error-prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS-based artificial neural network is trained using a combination of physical layer parameters such as BLER and LBW and application layer parameters of CT, SBR and FR. The proposed models are validated using unseen data set. The preliminary results show that good prediction accuracy was obtained from the models. This study should help in the development of a reference-free video prediction model and quality of service control methods for video over UMTS/WLAN networks.

Quality of experience (QoE) driven adaptation scheme for voice/video over IP

Network quality of service (NQoS) of IP networks is unpredictable and impacts the quality of networked multimedia services. Adaptive voice and video schemes are therefore vital for the provision of voice over IP (VoIP) services for optimised quality of experience (QoE). Traditional adaptation schemes based on NQoS do not take perceived quality into consideration even though the user is the best judge of quality. Additionally, uncertainties inherent in NQoS parameter measurements make the design of adaptation schemes difficult and their performance suboptimal. This paper presents a QoE-driven adaptation scheme for voice and video over IP to solve the optimisation problem to provide optimal QoE for networked voice and video applications. The adaptive VoIP architecture was implemented and tested both in NS2 and in an Open IMS Core network to allow extensive simulation and test-bed evaluation. Results show that the scheme was optimally responsive to available network bandwidth and congestion for both voice and video and optimised delivered QoE for different network conditions, and is friendly to TCP traffic.

Impact of Video Content on Video Quality for Video over Wireless Networks

Video streaming is a promising multimedia application and is gaining popularity over wireless/mobile communications. The quality of the video depends heavily on the type of content. The aim of the paper is threefold. First, video sequences are classified into groups representing different content types using cluster analysis based on the spatial (edges) and temporal (movement) feature extraction. Second, we conducted experiments to investigate the impact of packet loss on video contents and hence find the threshold in terms of upper, medium and lower quality boundary at which users’ perception of service quality is acceptable. Finally, to identify the minimum send bitrate to meet Quality of Serive (QoS) requirements (e.g. to reach communication quality with Mean Opinion Score (MOS) greater than 3.5) for the different content types over wireless networks. We tested 12 different video clips reflecting different content types. We chose Peak-Signal-to-Noise-Ratio (PSNR) and decodable frame rate (Q) as end-to-end video quality metrics and MPEG4 as the video codec. The work should help optimizing bandwidth allocation for specific content in content delivery networks.

An ANFIS-Based Hybrid Video Quality Prediction Model for Video Streaming over Wireless Networks

There are many parameters that affect video quality but their combined effect is not well identified and understood when video is transmitted over mobile/wireless networks. In this paper our aim is twofold. First, to study and analyze the behaviour of video quality for wide range variations of a set of selected parameters. Second, to develop a learning model based on ANFIS to estimate the visual perceptual quality in terms of the mean opinion score (MOS) and decodable frame rate (Q value). We trained three ANFIS-based ANNs for the three distinct content types using a combination of network level and application level parameters such as frame rate, send bitrate, link bandwidth and packet error rate and tested the ANN models using unseen dataset. We found that the video quality is more sensitive to network level parameters compared to application level parameters. Preliminary results show that a good prediction accuracy was obtained from the ANFIS-based ANN model. The work should help in the development of a reference-free video prediction model and quality of service (QoS) control methods for video over wireless/mobile networks.

QoE-Driven Sender Bitrate Adaptation Scheme for Video Applications over IP Multimedia Subsystem

IP Multimedia Subsystem (IMS) offers a framework which enables the provisioning of multimedia services with Quality of Service (QoS) and mobility support across heterogeneous networks. The aim of this paper is twofold. First, to present a new fuzzy logic based Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven for video applications. The scheme was tested and evaluated in the NS2 based simulation access networks of third generation Universal Mobile Telecommunication System (UMTS) networks. Second, to demonstrate the implementation of the proposed adaptation scheme in our developed open Android-based IMS test bed. The test bed was developed to fully understand and manipulate the effects of network conditions on perceptual quality. The SBR adaptive scheme is evaluated in terms of the Mean Opinion Score (MOS). Extensive simulation and test bed results demonstrate the effectiveness of the proposed adaptation scheme especially at UMTS bottleneck access networks where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.